U.S. patent application number 14/525125 was filed with the patent office on 2015-11-19 for optical safety monitoring with selective pixel array analysis.
The applicant listed for this patent is Rockwell Automation Technologies, Inc.. Invention is credited to Anne Bowlby, Richard Galera, Derek W. Jones, Francis L. Leard, Nilesh Pradhan.
Application Number | 20150334371 14/525125 |
Document ID | / |
Family ID | 53488116 |
Filed Date | 2015-11-19 |
United States Patent
Application |
20150334371 |
Kind Code |
A1 |
Galera; Richard ; et
al. |
November 19, 2015 |
OPTICAL SAFETY MONITORING WITH SELECTIVE PIXEL ARRAY ANALYSIS
Abstract
An imaging sensor device includes pixel array processing
functions that allow time-of-flight (TOF) analysis to be performed
on selected portions of the pixel array, while two-dimensional
imaging analysis is performed on the remaining portions of the
array, reducing processing load and response time relative to
performing TOF analysis for all pixels of the array. The portion of
the pixel array designated for TOF analysis can be pre-defined
through configuration of the imaging sensor device. Alternatively,
the imaging sensor device can dynamically select the portions of
the pixel array on which TOF analysis is to be performed based on
object detection and classification by the two-dimensional imaging
analysis. Embodiments of the imaging sensor device can also
implement a number of safety and redundancy functions to achieve a
high degree of safety integrity, making the sensor suitable for use
in various types of safety monitoring applications.
Inventors: |
Galera; Richard; (Nashua,
NH) ; Bowlby; Anne; (Lowell, MA) ; Jones;
Derek W.; (Galloway, GB) ; Pradhan; Nilesh;
(South Grafton, MA) ; Leard; Francis L.; (Sudbury,
MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Rockwell Automation Technologies, Inc. |
Mayfield Heights |
OH |
US |
|
|
Family ID: |
53488116 |
Appl. No.: |
14/525125 |
Filed: |
October 27, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62000483 |
May 19, 2014 |
|
|
|
Current U.S.
Class: |
348/46 |
Current CPC
Class: |
G06K 9/00201 20130101;
H04N 13/204 20180501; G06K 9/2054 20130101; G06K 9/00791
20130101 |
International
Class: |
H04N 13/02 20060101
H04N013/02 |
Claims
1. An imaging sensor device, comprising: a memory that stores
computer-executable components; a processor, operatively coupled to
the memory, that executes the computer-executable components, the
computer-executable components comprising: a pixel array component
configured to, for a pixel array of an image captured by the
imaging sensor device, group pixels of the pixel array to yield a
first subset of the pixels on which two-dimensional (2D) analysis
is to be performed and a second subset of the pixels on which
three-dimensional (3D) analysis is to be performed; an image
analysis component configured to perform 2D analysis on the first
subset of the pixels; and a distance determination component
configured to perform 3D analysis on the second subset of the
pixels.
2. The imaging sensor device of claim 1, wherein the image analysis
component is further configured to identify an object within the
image and determine a classification and a location of the object
based on the 2D analysis.
3. The imaging sensor device of claim 1, wherein the distance
determination component is further configured to generate distance
information for respective pixels of the second subset of the
pixels based on the 3D analysis.
4. The imaging sensor device of claim 1, further comprising an
illumination component configured to emit a beam of light pulses to
a viewing space monitored by the imaging sensor device.
5. The imaging sensor device of claim 4, further comprising a
waveform reconstruction component configured to, for a pixel of the
second subset of the pixels, generate waveform data representing a
reflected light pulse corresponding to the pixel, wherein the
distance determination component is configured to generate the
distance information based on the waveform data.
6. The imaging sensor device of claim 1, further comprising a
safety component configured to perform monitor one or more internal
components of the imaging sensor device a fault condition, and to
generate a safety output in response to detection of the fault
condition based on one or more safety algorithms.
7. The imaging sensor device of claim 6, further comprising a
hazardous analysis and decision component configured to generate at
least one of a control output or a message output based on a
correlation between a first result of the 2D analysis, a second
result of the 3D analysis, and the safety output.
8. The imaging sensor device of claim 7, wherein the hazardous
analysis and decision component is further configured to generate
correlated data comprising at least one of three-dimensional
location data, three-dimensional velocity data, three-dimensional
acceleration data, or three-dimensional trajectory data for an
object detected within the image based on the correlation between
the first result and the second result, and to generate at least
one of the control output or the message output based on the
correlated data.
9. The imaging sensor device of claim 8, wherein the hazardous
analysis and decision component is further configure to predict a
future location of the object within the viewing space based on at
least one of the three-dimensional location data, the
three-dimensional velocity data, the three-dimensional acceleration
data, or the three-dimensional trajectory data, and to generate at
least one of the control output or the message output based on the
future location.
10. The imaging sensor device of claim 1, wherein the pixel array
component is further configured to identify the second subset of
the pixels based on a configuration profile that defines one or
more portions of the pixel array on which the 3D analysis is to be
performed, and wherein the second subset of the pixels comprises
one of a single contiguous group of pixels or multiple
non-contiguous groups of pixels.
11. The imaging sensor device of claim 2, wherein the pixel array
component is further configured to select the second subset of the
pixels based on the classification and the location of the object
determined by the image analysis component, and wherein the second
subset of the pixels comprises one of a single contiguous group of
pixels or multiple non-contiguous groups of pixels.
12. A method for monitoring image data, comprising: collecting
image data by an imaging sensor device comprising at least one
processor; generating a pixel array based on the image data;
grouping pixels of the pixel array into at least one first pixel
group and at least one second pixel group; performing
two-dimensional (2D) imaging analysis on the at least one first
pixel group; and performing time-of-flight (TOF) analysis on the at
least one second pixel group.
13. The method of claim 12, wherein the performing the 2D imaging
analysis comprises: identifying an object within the image data;
and determining a location of the object within the image data.
14. The method of claim 12, wherein the performing the TOF analysis
yields distance information for respective pixels of the at least
one second pixel group.
15. The method of claim 13, wherein the grouping comprises
modifying at least one of a shape of the second pixel group or a
location of the second pixel group within the pixel array based on
the location and classification of the object.
16. The method of claim 13, further comprising: correlating a
result of the 2D analysis and a second result of the TOF analysis
to yield correlated information for the object; determining a
classification for the object based on the correlated information;
and controlling an output based on the correlated information and
the classification for the object.
17. The method of claim 16, wherein the correlating comprises
determining, as the correlated information, at least one of a
location of the object within a viewing space represented by the
image data, a velocity of the object within the viewing space, or a
trajectory of the object within the viewing space.
18. A non-transitory computer-readable medium having stored thereon
instructions that, in response to execution, cause an imaging
sensor device comprising a processor to perform operations, the
operations comprising: receiving image data representing an image
of a viewing space; determining values of respective pixels of a
pixel array based on the image data; identifying a first subset of
the pixels on which two-dimensional (2D) analysis is to be
performed; identifying a second subset of the pixels on which
time-of-flight (TOF) analysis is to be performed; performing the 2D
analysis on the first subset of the pixels; and performing the TOF
analysis on the second subset of the pixels.
19. The non-transitory computer-readable medium of claim 18,
further comprising identifying an object within the viewing space
and a classification of the object based on the 2D analysis.
20. The non-transitory computer-readable medium of claim 18,
wherein the identifying the second subset of the pixels comprises
identifying the second subset of the pixels based on identification
of the object and the classification of the object.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application Ser. No. 62/000,483, filed on May 19, 2014, entitled
"OPTICAL SAFETY MONITORING WITH SELECTIVE PIXEL ARRAY ANALYSIS,"
the entirety of which is incorporated herein by reference.
BACKGROUND
[0002] The subject matter disclosed herein relates generally to
optical area monitoring, and, more particularly, to an imaging
sensor capable of performing selective time-of-flight (TOF)
analysis on specified portions of a pixel array.
BRIEF DESCRIPTION
[0003] The following presents a simplified summary in order to
provide a basic understanding of some aspects described herein.
This summary is not an extensive overview nor is it intended to
identify key/critical elements or to delineate the scope of the
various aspects described herein. Its sole purpose is to present
some concepts in a simplified form as a prelude to the more
detailed description that is presented later.
[0004] In one or more embodiments, an imaging sensor device is
provided comprising a pixel array component configured to, for a
pixel array of an image captured by the imaging sensor device,
group pixels of the pixel array to yield a first subset of the
pixels on which two-dimensional (2D) analysis is to be performed
and a second subset of the pixels on which three-dimensional (3D)
analysis is to be performed; a 2D image analysis component
configured to perform 2D analysis on the first subset of the
pixels; and a distance determination component configured to
perform 3D analysis on the second subset of the pixels.
[0005] Also, one or more embodiments provide a method for
monitoring image data, comprising collecting image data by an
imaging sensor device comprising at least one processor; generating
a pixel array based on the image data; grouping pixels of the pixel
array into at least one first pixel group and at least one second
pixel group; performing two-dimensional (2D) imaging analysis on
the at least one first pixel group; and performing time-of-flight
(TOF) analysis on the at least one second pixel group.
[0006] Also, according to one or more embodiments, a non-transitory
computer-readable medium is provided having stored thereon
instructions that, in response to execution, cause an imaging
sensor device to perform operations, the operations, comprising
receiving image data representing an image of a viewing space;
determining values of respective pixels of a pixel array based on
the image data; identifying a first subset of the pixels on which
two-dimensional (2D) analysis is to be performed; identifying a
second subset of the pixels on which time-of-flight (TOF) analysis
is to be performed; performing the 2D analysis on the first subset
of the pixels; and performing the TOF analysis on the second subset
of the pixels.
[0007] To the accomplishment of the foregoing and related ends,
certain illustrative aspects are described herein in connection
with the following description and the annexed drawings. These
aspects are indicative of various ways which can be practiced, all
of which are intended to be covered herein. Other advantages and
novel features may become apparent from the following detailed
description when considered in conjunction with the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a schematic illustrating 2D detection of an object
in the X and Y dimensions using a two-dimensional imaging
sensor.
[0009] FIG. 2A is a schematic illustrating 2D image analysis of an
image using a 2D image sensor.
[0010] FIG. 2B is a schematic illustrating 3D image analysis of an
image using a 3D image sensor.
[0011] FIG. 3 is a block diagram of an example imaging sensor
device.
[0012] FIG. 4 is a functional block diagram illustrating an
overview of an imaging sensor device's operations.
[0013] FIG. 5 is a block diagram illustrating components of an
imaging sensor device.
[0014] FIG. 6. is an illustration of example pixel array
groupings.
[0015] FIG. 7 is a block diagram illustrating correlation of 2D
(imaging) and 3D (distance) information by an imaging sensor
device.
[0016] FIG. 8 is a block diagram of an example safety component
that can be integrated in one or more embodiments of an imaging
sensor device.
[0017] FIG. 9 is a schematic of an industrial safety monitoring
system that utilizes an imaging sensor device.
[0018] FIG. 10 is an illustration of an example automotive safety
application that employs an imaging sensor device.
[0019] FIG. 11 is a flowchart of an example methodology for
performing selecting three-dimensional analysis on a pixel array by
an imaging sensor device.
[0020] FIG. 12 is a flowchart of an example methodology for
dynamically selecting a portion of a pixel array for selective 3D
analysis.
[0021] FIG. 13 is an example computing environment.
[0022] FIG. 14 is an example networking environment.
DETAILED DESCRIPTION
[0023] The subject disclosure is now described with reference to
the drawings, wherein like reference numerals are used to refer to
like elements throughout. In the following description, for
purposes of explanation, numerous specific details are set forth in
order to provide a thorough understanding thereof. It may be
evident, however, that the subject disclosure can be practiced
without these specific details. In other instances, well-known
structures and devices are shown in block diagram form in order to
facilitate a description thereof.
[0024] As used in this application, the terms "component,"
"system," "platform," "layer," "controller," "terminal," "station,"
"node," "interface" are intended to refer to a computer-related
entity or an entity related to, or that is part of, an operational
apparatus with one or more specific functionalities, wherein such
entities can be either hardware, a combination of hardware and
software, software, or software in execution. For example, a
component can be, but is not limited to being, a process running on
a processor, a processor, a hard disk drive, multiple storage
drives (of optical or magnetic storage medium) including affixed
(e.g., screwed or bolted) or removable affixed solid-state storage
drives; an object; an executable; a thread of execution; a
computer-executable program, and/or a computer. By way of
illustration, both an application running on a server and the
server can be a component. One or more components can reside within
a process and/or thread of execution, and a component can be
localized on one computer and/or distributed between two or more
computers. Also, components as described herein can execute from
various computer readable storage media having various data
structures stored thereon. The components may communicate via local
and/or remote processes such as in accordance with a signal having
one or more data packets (e.g., data from one component interacting
with another component in a local system, distributed system,
and/or across a network such as the Internet with other systems via
the signal). As another example, a component can be an apparatus
with specific functionality provided by mechanical parts operated
by electric or electronic circuitry which is operated by a software
or a firmware application executed by a processor, wherein the
processor can be internal or external to the apparatus and executes
at least a part of the software or firmware application. As yet
another example, a component can be an apparatus that provides
specific functionality through electronic components without
mechanical parts, the electronic components can include a processor
therein to execute software or firmware that provides at least in
part the functionality of the electronic components. As further yet
another example, interface(s) can include input/output (I/O)
components as well as associated processor, application, or
Application Programming Interface (API) components. While the
foregoing examples are directed to aspects of a component, the
exemplified aspects or features also apply to a system, platform,
interface, layer, controller, terminal, and the like.
[0025] As used herein, the terms "to infer" and "inference" refer
generally to the process of reasoning about or inferring states of
the system, environment, and/or user from a set of observations as
captured via events and/or data. Inference can be employed to
identify a specific context or action, or can generate a
probability distribution over states, for example. The inference
can be probabilistic--that is, the computation of a probability
distribution over states of interest based on a consideration of
data and events. Inference can also refer to techniques employed
for composing higher-level events from a set of events and/or data.
Such inference results in the construction of new events or actions
from a set of observed events and/or stored event data, whether or
not the events are correlated in close temporal proximity, and
whether the events and data come from one or several event and data
sources.
[0026] In addition, the term "or" is intended to mean an inclusive
"or" rather than an exclusive "or." That is, unless specified
otherwise, or clear from the context, the phrase "X employs A or B"
is intended to mean any of the natural inclusive permutations. That
is, the phrase "X employs A or B" is satisfied by any of the
following instances: X employs A; X employs B; or X employs both A
and B. In addition, the articles "a" and "an" as used in this
application and the appended claims should generally be construed
to mean "one or more" unless specified otherwise or clear from the
context to be directed to a singular form.
[0027] Furthermore, the term "set" as employed herein excludes the
empty set; e.g., the set with no elements therein. Thus, a "set" in
the subject disclosure includes one or more elements or entities.
As an illustration, a set of controllers includes one or more
controllers; a set of data resources includes one or more data
resources; etc. Likewise, the term "group" as utilized herein
refers to a collection of one or more entities; e.g., a group of
nodes refers to one or more nodes.
[0028] Various aspects or features will be presented in terms of
systems that may include a number of devices, components, modules,
and the like. It is to be understood and appreciated that the
various systems may include additional devices, components,
modules, etc. and/or may not include all of the devices,
components, modules etc. discussed in connection with the figures.
A combination of these approaches also can be used.
[0029] Two-dimensional (2D) imaging sensors are generally used to
detect and identify shape and/or surface characteristics of objects
within a viewing field of the sensor. FIG. 1 illustrates
identification of an object using a 2D imaging sensor 104. Some
types of 2D imaging sensors (e.g., imaging cameras) operate by
projecting a wide, light beam 106 toward an area to be monitored
and collecting the reflected light reflected from the surfaces and
objects (e.g., object 108) within the viewing area at a receiver.
Some sensors may sweep the light beam 106 across the viewing area
in an oscillatory manner to collect line-wise image data, which is
analyzed to identify object edges and surfaces, surface patterns,
or other such information. Alternatively, the sensor 104 may
project a stationary, substantially planar beam of light across an
area of interest and collect data on objects that pass through the
beam. In general, 2D image sensors perform grayscale or
red-green-blue (RGB) analysis on the pixel data generated based on
the reflected light to yield two-dimensional image data for the
viewing field, which can be analyzed to identify object edges,
object surface patterns or contours, or other such information.
FIG. 2A is a schematic illustrating 2D image analysis of an image
206 using a 2D image sensor 202. 2D image analysis yields object
and surface information in the x-y plane. Depending on the
particular application in which the imaging sensor is being used,
the sensor will generate suitable outputs based on the objects
and/or patterns detected within the viewing area.
[0030] Three-dimensional (3D) image sensors, also known as
time-of-flight (TOF) sensors, are designed to generate distance
information as well as two-dimensional shape information for
objects and surfaces within the sensor's viewing field. Some types
of TOF sensors determine a distance of an object using phase shift
monitoring techniques, whereby a beam of light is emitted to the
viewing field, and the measured phase shift of light reflected from
the object relative to the emitted light is translated to a
distance value. Other types of TOF sensors that employ pulsed light
illumination measure the elapsed time between emission of a light
pulse to the viewing field and receipt of a reflected light pulse
at the sensor's photo-receiver. Since this time-of-flight
information is a function of the distance of the object or surface
from the sensor, the sensor is able to leverage the TOF information
to determine the distance of the object or surface point from the
sensor. FIG. 2B a schematic illustrating 3D image analysis of an
image 208 using a 3D image sensor 204. As shown in this figure, 3D
analysis yields distance or depth information in the z-direction
(that is, the distance of objects and surfaces from the sensor 204)
as well as imaging information in the x-y plane.
[0031] Three-dimensional image analysis--which entails measurement
of time-of-flight information and subsequent calculation of
distance information--is generally more processing intensive than
2D image analysis. The additional processing time and power
required for 3D analysis may render 3D image sensors unsuitable for
certain types of applications that require fast, reliable response
times. However, there are certain types of applications that could
benefit from 3D image analysis, but which require fast and reliable
decision-making and response times. For example, industrial safety
monitoring applications must be able to reliably detect the
presence of human beings within a potentially hazardous area, and
to respond with appropriate safety control outputs (e.g., commands
to stop or slow a running machine, to remove power from hazardous
machinery, etc.) with minimal delay to prevent injury.
[0032] To address these and other issues, one or more embodiments
of the present disclosure provide an imaging sensor capable of
performing 3D image analysis on selected subsets or portions of the
sensor's pixel array. In one or more embodiments, the imaging
sensor allows one or more specified portions of the pixel array to
be selected for 3D (time-of-flight) analysis in order to obtain
distance information for pixels in that portion of the pixel array,
while the remaining pixel array areas will be processed using 2D
image analysis. For example, after the imaging sensor is trained on
the area of interest, a user may select a horizontal stripe of
pixels across a middle section (or an upper or lower edge) of the
pixel array for 3D analysis, so that distance information as well
as object identification information can be obtained and managed
for the area corresponding to the selected stripe of pixels. The
imaging sensor will apply 2D analysis (e.g., grayscale or RGB
analysis) to the remaining, non-selected areas of the pixel array
in order to detect, identify, classify, and/or correlate objects
within the viewing area. Since 2D imaging processes more quickly
than 3D processing, processing load is reduced and sensor response
time is improved by limiting 3D analysis to only those areas of the
scene for which distance information is required. The imaging
sensor can also be configured to correlate results of the 2D and 3D
analysis so that the identity, speed, distance, and trajectory of
an object within the viewing space can be obtained with a high
level of safety integrity.
[0033] In some embodiments, the imaging sensor may be configured to
dynamically select or modify the portion of the pixel array to
which 3D analysis is to be applied; e.g., based on detection of an
object within the viewing area that satisfies one or more criteria.
For example, during normal operation, the imaging sensor may be
configured to perform continuous 2D analysis on the entire pixel
array until an object or collection of objects having a certain
defined classification (e.g., a person, a trolley, etc.) is
detected. When such an object is detected--e.g., when a person
enters the viewing area of the sensor--the sensor may define a
portion of the pixel array corresponding to an area around the
object for 3D analysis, so that TOF (distance) information for the
object can be tracked. The imaging sensor may dynamically change
this defined pixel area to move with object so that distance and
speed information can be monitored for the object as long as the
object remains within the viewing area.
[0034] In one or more embodiments, the imaging sensor may also be
configured to, for a given image, identify non-contiguous groups of
pixels that belong to a single object of a defined classification.
This can allow the imaging sensor to identify the presence of a
person within the viewing area even if the person is partially
obscured within the image. For example, the imaging sensor may be
trained to identify the presence of two separate visible objects
corresponding to human legs, and to correlate these two objects
within the image as belonging to a human being who is within the
viewing area but partially obscured. The sensor can track these
correlated objects as necessary (e.g., by performing 3D analysis on
the pixel areas corresponding to the two objects) so that
appropriate safety output or feedback information can be generated
based on the location and speed of the person within the area.
[0035] FIG. 3 is a block diagram of an example imaging sensor
device 302 according to one or more embodiments of this disclosure.
Although FIG. 3 depicts certain functional components as residing
on imaging sensor device 302, it is to be appreciated that one or
more of the functional components illustrated in FIG. 3 may reside
on a separate device relative to imaging sensor device 302 in some
embodiments. Aspects of the systems, apparatuses, or processes
explained in this disclosure can constitute machine-executable
components embodied within machine(s), e.g., embodied in one or
more computer-readable mediums (or media) associated with one or
more machines. Such components, when executed by one or more
machines, e.g., computer(s), computing device(s), automation
device(s), virtual machine(s), etc., can cause the machine(s) to
perform the operations described.
[0036] Imaging sensor device 302 can include an illumination
component 304, a pixel array component 306, a distance
determination component 310, an image analysis component 312, a
hazard analysis and decision component 314, a safety component 316,
one or more processors 318, and memory 320. In various embodiments,
one or more of the illumination component 304, pixel array
component 306, distance determination component 310, image analysis
component 312, hazard analysis and decision component 314, safety
component 316, the one or more processors 318, and memory 320 can
be electrically and/or communicatively coupled to one another to
perform one or more of the functions of the imaging sensor device
302. In some embodiments, components 304, 306, 310, 312, 314, and
316 can comprise software instructions stored on memory 320 and
executed by processor(s) 318. Imaging sensor device 302 may also
interact with other hardware and/or software components not
depicted in FIG. 2. For example, processor(s) 318 may interact with
one or more external user interface devices, such as a keyboard, a
mouse, a display monitor, a touchscreen, or other such interface
devices. Imaging sensor device 302 may also include network
communication components and associated networking ports for
sending data generated by any of components 304, 306, 310, 312,
314, and 316 over a network (either or both of a standard data
network or a safety network), or over a backplane.
[0037] Illumination component 304 can be configured to control
emission of light by the sensor device. Imaging sensor device 302
may comprise a laser or light emitting diode (LED) light source
under the control of illumination component 304. In some
embodiments, illumination component 304 may generate pulsed light
emissions directed to the viewing field, so that time-of-flight
information for the reflected light pulses can be generated by the
sensor device. The pixel array component 306 can be configured to
process and analyze a pixel array corresponding to an image of the
viewing field monitored by the sensor device. For example, the
pixel array component 306 may control which subset of pixels will
be processed using 3D analysis. The subset of pixels to which 3D
analysis is to be applied may be fixed (e.g., preconfigured via
user input); alternatively, the pixel array component 306 may
select the subset of pixels for 3D analysis dynamically according
to one or more defined criteria (e.g., human or facial recognition,
object classification, etc.).
[0038] Distance determination component 310 can be configured to
derive distance information by performing 3D analysis on all or
selected portions of the pixel array data. Any suitable analysis
technique can be implemented by distance determination component,
including but not limited to phase shift monitoring or pulsed time
of flight analysis.
[0039] The image analysis component 312 can be configured to
perform 2D analysis on portions of the pixel array that have not
been selected for 3D analysis. The hazard analysis and decision
component 314 can be configured to analyze and control one or more
sensor outputs based on results generated by the pixel array
component 306, distance determination component 310, image analysis
component 312, and the safety component 316. This can include, for
example, sending a control signal to a control or supervisory
device (e.g., an industrial controller, an on-board computer
mounted in a mobile vehicle, etc.) to perform a control action,
initiating a safety action (e.g., removing power from a hazardous
machine, switching an industrial system to a safe operating mode,
etc.), sending a feedback message to one or more plant personnel
via a human-machine interface (HMI) or a personal mobile device,
sending data over a safety network, or other such output. Safety
component 316 can be configured to implement one or more safety
and/or redundancy features within the imaging sensor device 302 to
render the sensor device suitable for use in safety applications
(e.g., industrial safety applications designed to monitor a
hazardous area and reliably perform automated control actions to
mitigate risk of injury in response to detection of a potentially
unsafe human presence or action, automobile safety applications in
which one or more imaging sensors mounted on a vehicle control
breaking of the vehicle based on detected risk conditions, etc.).
By implementing such safety and redundancy functions, the imaging
sensor device 302 can monitor a two-dimensional plane and a
three-dimensional volume and respond to detected conditions with a
high safety integrity level (e.g., SIL or ASIL), making the sensor
device suitable for use in some safety application as an
alternative to light curtains or other such sensors.
[0040] The one or more processors 318 can perform one or more of
the functions described herein with reference to the systems and/or
methods disclosed. Memory 320 can be a computer-readable storage
medium storing computer-executable instructions and/or information
for performing the functions described herein with reference to the
systems and/or methods disclosed.
[0041] FIG. 4 is a functional block diagram illustrating an
overview of the imaging sensor device's operations. Optics block
402 includes the light emitter (e.g., a laser, LED, or remote
phosphor emitter) for projecting a light beam to the monitored
scene 416 and an array of photo-receivers for receiving reflected
light pulses from objects and surfaces within the scene.
Illumination block 404 controls the projection of light by the LED,
laser, or remote phosphor laser light source. In some embodiments,
the illumination block 404 may project a beam of light or light
pulses to achieve a uniform illumination across the scene 416.
Alternatively, the illumination block may implement patterned
illumination for 3D analysis, whereby light is concentrated in
spots that are spaced across the scene 416 to ensure detection of
objects of a given minimum size. This illumination technique can
ensure accurate object detection at increased distances without
increasing the power of the light source. Alternatively, the
illumination block 404 may project light to achieve a uniform
illumination across the scene 416.
[0042] Upon receipt of reflected light at the photo-receivers of
the imaging sensor device 302, pixel data is generated based on the
light intensity measured at each photo-receiver, and pixel array
block 406 performs processing on the resulting pixel array data
comprising the image. This can include, for example, identifying a
first subset of pixels in the array on which 3D processing is to be
performed, and designating a remaining second subset of pixels for
2D imaging analysis. Subsequent processing of each pixel depends
upon the type of analysis (2D or 3D) to be performed on that
pixel.
[0043] For a pixel selected for 3D (distance or depth) analysis, 3D
distance analysis 410 determines a distance of an object or surface
in the viewing field corresponding to the pixel, e.g., using phase
shift time-of-flight analysis on a light beam reflected by the
object, or using pulsed time-of-flight analysis on a light pulse
reflected from the object. Performing distance calculations for
each pixel of the 3D analysis portion(s) of the pixel array yields
a 3D point cloud for the selected areas of the viewing field.
[0044] 2D imaging block 412 performs 2D image analysis on the
portion(s) of the pixel array for which 3D analysis is not
performed. 2D image analysis can comprise RGB or grayscale analysis
of the image portions corresponding to the non-3D pixels, including
but not limited to edge detection, contour analysis, image
sharpening, contrast adjustment, difference and additive imaging,
etc. The imaging sensor device 302 can employ 2D image analysis to
identify objects within the viewing area and determine whether the
identified objects correspond to one or more defined object
classifications (e.g., a human being, a forklift or trolley, a
machined part on a conveyor, a pallet containing packaged products,
etc.). In some embodiments, the imaging sensor device 302 may also
be configured to perform facial recognition using 2D image
analysis, which is useful for applications in which a control
decision or operator feedback output is dependent upon an identity
of the person detected within the viewing field.
[0045] Imaging sensor device 302 can correlate results of the 2D
and 3D analysis to yield object data at object data block 414.
Object data can include, for example, a location, speed, an
acceleration and/or trajectory of an identified object within the
three-dimensional viewing space. Depending on the type of
application, a hazard analysis and decision block 418 can generate
suitable outputs or operator feedback based on the correlated
object data. In some embodiments, imaging sensor device 302 can
interface with an industrial control or safety system, a vehicle
safety system, or other such system to implement control features
based on object detection. Accordingly, outputs generated by the
sensor device can include control instructions to an associated
control or safety system (e.g., a programmable logic controller or
other safety automation controller, an engine control unit of a
mobile vehicle, etc.) to alter operation of a machine or system
based on the object data, safety outputs to an associated safety
system (e.g., a safety relay) that place an industrial system in a
safe state based on the presence and movements of a human being
within the viewing field, or other such outputs. Imaging sensor
device can also include a safety block 420 that monitors and
diagnoses internal components and faults of the sensor device,
including but not limited to power monitoring, vibration
monitoring, and temperature monitoring. Accordingly, control
outputs and messages generated by the hazard analysis and decision
block 418 can additionally be a function of the diagnosis results
generated by the safety block 420.
[0046] FIG. 5 is a block diagram illustrating components of imaging
sensor device 302 according to one or more embodiments. In this
example, illumination component 304 controls emission of LED,
laser, or remote phosphor light to the viewing field via emitter
506. In some embodiments, illumination component 304 can project a
wide, substantially planar beam of pulsed LED illumination to the
viewing field. For scanning type devices, illumination component
304 can sweep this planar beam over an angular range across the
viewing area in an oscillatory manner to facilitate collection of
image data over the entire viewing range. In other embodiments, the
beam may remain static (trained in a fixed direction) so that
objects can be detected and identified as they pass through the
plane of the beam. In yet another example, illumination component
304 may project a wide beam of light pulses over the viewing field
(e.g., a cone-shaped beam).
[0047] In some embodiments, illumination component 304 may
uniformly illuminate the viewing field using a laser, LED, or
remote phosphor light source. Alternatively, some embodiments of
illumination component 304 may employ a patterned illumination
technique whereby, rather than uniformly illuminating the viewing
area, the illumination component 304 concentrates light in spots
spaced with a certain distance over the viewing area. This
technique can improve reliability of detection of small objects and
of objects with low reflectivity. In such embodiments, the size of
each spot of light can be defined based on the effective size of
the pixels and the optical characteristics of the receiving element
508 of the sensor device. The receiving element 508 is sized
relative to the spot size such that the image of a spot on the
receiving element 508 covers at least the light sensitive area of
one pixel. In a variation of this technique, the illumination
component 304 or the lens design can also be configured to modulate
the illumination intensity of the emitted spots, such that high
brightness spots and low brightness spots are interlaced across the
viewing area simultaneously. This technique can facilitate reliable
detection of bright and dark objects within a single image frame.
In an example implementation, the focused spots of illumination can
be achieved by placing a squared lenslet comprising square or
rectangular apertures in front of the LED, laser, or remote
phosphor light source. The locations of the apertures on the
lenslet define the spot pattern. To ensure accurate detection with
small object sizes, the spot pattern can be defined such that at
least two horizontal spots and two vertical spots cover the minimum
size of object at the given distance from the lens element 508.
[0048] Lens element 508 receives light reflected from the viewing
field, and pixel array component 306 performs processing on the
pixels of the resulting image data. As noted above, imaging sensor
device 302 allows portions of the resulting pixel array 502 to be
selected for 3D (distance or depth) processing and analysis, while
the remaining portions of the pixel array are processed using 2D
(imaging) analysis. In the example depicted in FIG. 5, a horizontal
band 512 across a middle section of the pixel array 502 has been
selected for 3D analysis, while the remaining portions of the pixel
array 502 above and below the selected band 512 will be processed
using 2D analysis. In some embodiments, pixel array component 306
identifies and groups the pixels into 2D and 3D sections based on a
predefined configuration profile 510 specifying one or more areas
of the pixel array 502 for which 3D analysis is to be performed.
Alternatively, pixel array component 306 may be configured to
dynamically select the areas of the pixel array on which 3D
analysis is to be performed, as will be described in more detail
below.
[0049] Although FIG. 5 depicts the area of 3D processing as a
single horizontal band across the middle of the pixel array, it is
to be appreciated that substantially any manner of pixel grouping
can be managed by pixel array component 306. FIG. 6 illustrates
other example pixel groupings. In addition to the single horizontal
band depicted in pixel array 602, pixels may also be grouped into
multiple 3D bands (either horizontal or vertical), as shown in
example pixel array 604. Pixel array 606 depicts a split-screen
type of pixel grouping, in which a left-side portion of the pixel
array is selected for 3D analysis, while 2D analysis is performed
on the right-side portion. Pixels may also be grouped into
non-contiguous pixel clusters of various sizes, as shown in example
pixel array 608.
[0050] In an example scenario wherein the imaging sensor device 302
is used to monitor an area of an industrial facility, it may be
known that certain areas of the viewing field correspond to
potentially hazardous zones, while other areas of the viewing field
correspond to safe zones that pose little or no risk to operators.
Accordingly, a system designer may define a section of the pixel
array that encompasses the known hazardous areas for 3D analysis.
These pixel area definitions can be stored in configuration profile
510 and leveraged by pixel array component 306 to group pixels of
the pixel array accordingly for group analysis. Portions of the
pixel array 502 that are not selected for 3D analysis will be
processed using 2D analysis, which is less computationally
intensive than 3D analysis. By limiting 3D analysis to crucial
subsets of the pixel array 502 and performing 2D analysis on the
remaining portions of the array, overall processing time can be
reduced relative to performing 3D analysis on the entire image.
[0051] In another example, a ceiling-mounted imaging sensor may be
oriented to face downward with the line of site substantially
perpendicular to the floor, in order to monitor traffic through an
entrance gate to a room or zone of interest. In this example, it
may only be necessary to perform 3D analysis on a middle band of
the pixel array corresponding to the pathway to the entrance gate.
Accordingly, a system designer can define this area of the pixel
array 502 and save these settings in the configuration profile
510.
[0052] Imaging sensor device 302 can support any suitable technique
for allowing a user to define 3D zones on the pixel array 502. For
example, an interface application executable on a personal
computing device (e.g., tablet computer, laptop computer, desktop
computer, mobile phone, etc.) may be used to facilitate data
exchange between the computing device and the imaging sensor device
302. The interface application can generate and render
configuration display screens capable of receiving input data from
a user that set configuration parameters and definitions for the
sensor. One or more configuration display screens may allow a user
to define the areas of 3D analysis by entering x-y coordinates that
define the sections of the pixel array 502 for which 3D analysis is
to be performed. Alternatively, the configuration display screens
may allow the user to draw (using a mouse or stylus) boundary lines
(either linear or curved) that define the areas of 3D analysis. If
the imaging sensor device 302 has been trained on the viewing area,
the configuration screens can display a live image or a screenshot
of the viewing area and allow the user to draw the 3D analysis
boundary lines as an overlay on the image or screenshot.
[0053] Upon receipt of live pixel array data, and after the pixel
array component 306 has grouped the pixels into respective 3D and
2D zones, image analysis component 312 performs 2D imaging analysis
on those portions of pixel array 502 that were not designated by
pixel array component 306 for 3D analysis. As noted above, imaging
sensor device 302 can employ 2D imaging analysis to identify and
classify objects within the image frame. Classification of objects
can be based on pre-defined classes of objects that the imaging
sensor device 302 has been trained to identify, including but not
limited to human beings, particular types of vehicles (e.g.,
forklifts, trolleys, etc.), a manufactured part, a pallet, or other
such object classifications.
[0054] In some embodiments, one or both of the pixel array
component 306 or the image analysis component 312 can be configured
to recognize instances in which two or more non-contiguous groups
of pixels of the pixel array 502 belong to a common object or
person that may be partially obscured within the image. In an
example scenario, an operator may enter the image frame, but may be
partially obscured by another object within the frame such that
only portions of the operator's legs or feet are directly visible
to the sensor device. The pixels of the pixel array 502
corresponding to the operator's left and right legs or feet may
comprise separate, non-contiguous pixel groups, since the operator
is obscured above the knees. The image analysis component 312 may
be trained recognize lower-body human features, and therefore
recognizes that two separate detected objects identified as human
legs which are oriented a certain way with respect to one another
within the frame belong to a common person, and are indicative of a
human presence within the image. Accordingly, image analysis
component 312 can identify and classify the two objects as human
legs, and instruct the pixel array component 306 to associate the
two detected objects for collective analysis under the assumption
that the two objects correspond to a human being.
[0055] Concurrently or in coordination with the 2D image analysis,
distance determination component 310 can perform 3D analysis on the
pixels comprising the defined 3D portion of the pixel array 502 to
determine a distance value associated with each of those pixels.
The distance value represents the distance of the object or surface
corresponding to the pixel from the sensor device. The analysis
technique employed by the distance determination component 310
depends on the type of illumination and 3D analysis supported by
the device. For example, for imaging sensor devices that employ
phase shift analysis, the distance determination component 310 can
monitor the phase shift of a reflected light beam received at a
photo-receiver and compare this phase shift with the phase of the
light beam emitted by the illumination component 304. The distance
is then determined as a function of the relative phase shift
between the emitted and received light. Other types of imaging
sensor that employ pulsed light illumination measure the time
duration between emission of a light pulse by the illumination
component and receipt of a reflected light pulse at the
photo-receiver for each pixel, and determining the distance as a
function of this duration. In such embodiments, the distance
determination component 310 may monitor the electrical output of
the photo-receiver (which is a function of the intensity of light
incident on the surface of the photo-receiver) and generate a
waveform representing the reflected light pulse. The front edge of
the returned light pulse can then be identified based on analysis
of the waveform data, which represents the time at which the light
pulse was received at the lens element 508. The distance
determination component 310 can then compare this time with the
time at which the emitted light pulse was sent by the illumination
component 304. The difference between the two times represents the
time-of-flight for the pulse, from which the distance information
for the pixel corresponding to the photo-receiver can be derived.
By performing waveform reconstruction and distance determination
for each pixel in the 3D analysis portions of the pixel array 502,
a 3D point cloud can be derived for the selected areas of the pixel
array 502.
[0056] Some embodiments of imaging sensor device 302 may support
dynamic definition of 3D analysis zones based on object detection
and classification by the 2D image analysis component. For example,
during normal operation the imaging sensor device 302 may perform
2D analysis on the entire pixel array 502 until an object of a
specified classification is detected within the viewing field. In
response to detection of such an object (e.g., a person, a vehicle,
etc.) within the viewing field, the image analysis component 312
may provide information to pixel array component 306 identifying
the object and its location within the pixel array 502. Pixel array
component 306 can then define one or more pixel groups
corresponding to the identified object, and instruct distance
determination component 310 to begin performing 3D analysis on
those groups of pixels, so that both the location and distance of
the object can be tracked. In some embodiments, the pixel array
component 306 and image analysis component 312 can operate in
conjunction to move the defined 3D analysis portion of the pixel
array 502 to track with the detected object as long as the object
remains within the frame. Thus, embodiments of the imaging sensor
device 302 can use 2D imaging analysis to recognize objects of
interest within the frame, and instruct pixel array component 306
where 3D analysis should be performed. In this way, the imaging
sensor device 302 can continuously collect TOF information for
objects of interest while substantially minimizing the areas of the
pixel array 502 on which 3D analysis is performed, optimizing
processing and response times.
[0057] The imaging sensor device 302 can correlate results of the
2D and 3D analyses and determine suitable control or messaging
outputs based on object classification, location, velocity, and/or
trajectory. FIG. 7 illustrates correlation of 2D (imaging) and 3D
(distance) information by the imaging sensor device. As described
above, image analysis component 312 can generate 2D analysis
results 704, including but not limited to object recognition or
classification, x-y location of objects, correlation of pixel
groups determined to belong to a common object, human and/or facial
recognition based on image analysis, and other such data. Distance
determination component 310 generates 3D analysis results 702
(time-of-flight distance information) for each pixel, yielding a 3D
point cloud for areas of interest (areas of the pixel array
specified for selective 3D analysis, either manually by a system
designer or dynamically by the sensor based on information provided
by image analysis component 312, as described in previous
examples). The imaging sensor can correlate all or selected
portions of these data sets to yield correlated results 706. These
correlated results can include, but are not limited to, object
location, velocity, and trajectory within the three-dimensional
space; a predicted future location of an object of interest based
on the three-dimensional location, velocity, and trajectory; or
other such information.
[0058] In a non-limiting example of 2D and 3D result correlation,
image analysis component 312 may identify objects within the image
frame that correspond to a class of objects for which 3D analysis
is required. In response, the imaging sensor device can apply 3D
analysis to the region of the pixel array 502 corresponding to the
detected object to obtain distance information for the object over
time, while 2D analysis can track the x-y location of the object
within the frame. By correlating these results, the object's
instantaneous position, velocity, acceleration, and trajectory
within the three-dimensional viewing space can be determined. For
embodiments in which the imaging sensor device supports prediction
of future object position, the sensor may also determine whether
the object is predicted to be within a particular subspace of the
three-dimensional viewing field based on the current location,
speed, and trajectory, and generate a control or feedback output
based on risk analysis using this prediction.
[0059] In another example, the imaging sensor device may coordinate
object classification and edge detection (2D analysis results) with
depth analysis (a 3D analysis result) in order to obtain depth
information for all pixels enclosed within the edges of an
identified object. For example, when an object enters the viewing
field, the imaging sensor may leverage 2D imaging analysis to
identify and classify the object as corresponding to a defined
object class requiring 3D analysis. The 2D analysis may further
include edge detection, which identifies the visible edges or
boundaries of the object. The imaging sensor can then perform
selective 3D analysis on all pixels within the object boundaries
identified via 2D analysis.
[0060] Returning now to FIG. 4, based on the particular application
being executed by the sensor device, hazard analysis and decision
component 314 can be instructed to generate a suitable control,
safety, or feedback output when the object classification,
position, speed, acceleration, and/or trajectory satisfy a defined
criterion. In some embodiments, hazard analysis and decision
component 314 may interface with a control device (e.g., an
industrial controller, a safety relay, an on-board computer for a
motor vehicle, etc.) over a hardwired or networked connection, and
issue control instructions to the control device based on identity,
position, and behavior of objects observed in the viewing field. In
an example scenario, based on correlation of analysis results
generated by the distance determination component 310 and the image
analysis component 312, the imaging sensor device 302 may identify
that a plant employee has entered the viewing field, and that the
employee's current location, speed, acceleration, and trajectory
may place the employee within a potentially hazardous area near a
controlled industrial machine. In response, the hazard analysis and
decision component 314 is instructed to issue a command to the
industrial controller to place the machine in a safe mode (e.g., by
placing the machine in an idle mode or a slowed operation mode, or
by instructing a safety relay to remove power from certain movable
components of the machine). In another example scenario, the hazard
analysis and decision component 314 may be configured to generate
feedback information to be rendered on a display device based on
object identification and behavior. This can include, for example,
customized warning messages recommending that a user follow an
alternate path or relocate to a safe area within the monitoring
area. For embodiments of the imaging sensor device 302 that support
facial recognition, feedback messages generated by hazard analysis
and decision component 314 may also be further customized based on
an identity of the employee detected within the viewing field.
Hazard analysis and decision component 314 may interface with a
display device mounted within the monitored area, or may be
targeted to a personal device associated with the identified
employee.
[0061] The object detection and tracking features described above,
together with the reduced processing load and commensurate
improvement in decision-making and response time that results from
minimizing the amount of 3D processing required, render the imaging
sensor devices described herein suitable for safety applications,
which require a high degree of safety integrity and fast response
times in order to mitigate risk of injuries. To ensure safety
integrity of the imaging sensor device, one or more embodiments may
include a safety component 316 that implements one or more features
for ensuring reliability and accuracy of the sensor in a range of
operating conditions, improving the safety integrity of the sensor
device. In general, safety component 316 is configured to perform
fault monitoring and diagnostic analysis on a range of conditions
that may impact the integrity of the sensor operation, and trigger
actions designed to mitigate hazards that may arise when a
monitored deviates from a safe state (e.g., instruct the hazard
analysis and decision component 314 to switch a machine to a safe
state, output a warning message, etc.) FIG. 8 illustrates an
example safety component 316 that can be integrated in one or more
embodiments of imaging sensor device 302. Safety component 316 can
comprise one or more sub-components that perform various types of
diagnostics and fault monitoring. FIG. 8 illustrates an example
safety component that includes functionality for monitoring and
compensating for temperature, power, vibration, and internal
component faults. However, it is to be appreciated that other types
of fault monitoring and diagnostic capabilities may be supported by
various embodiments of safety component 316, and are within the
scope of this disclosure.
[0062] Temperatures within the sensor device may have an impact on
the distance values generated by the sensor components.
Accordingly, safety component 316 can include a temperature control
component 802 configured to adjust the distance values generated by
distance determination component 310 to compensate for measured
deviations in temperature. Some embodiments of temperature control
component 802 can also include mechanisms to regulate the sensor's
internal temperature to maintain a specified optimal operating
temperature, as well as redundant fault detection mechanisms to
ensure that the temperature compensation meets or exceeds a defined
minimum safety integrity level (e.g. SIL 2, SIL 3, ASIL C, ASIL D,
etc.).
[0063] Safety component 316 can also include a power monitoring
component 804 configured to monitor the internal rails that provide
power to crucial components, and perform compensation actions in
response to detected voltage deviations from rated tolerances. In
this regard, some embodiments of imaging sensor device 302 may
include a redundant power supply to ensure that a failure of the
main supply does not prevent continued operation of the sensor
device. Vibration compensation component 806 can be configured to
perform appropriate compensation actions in response to monitored
vibrations induced on the sensor.
[0064] Fault detection component 808 can be configured to monitor
and diagnose internal sensor faults, and to generate information or
instructions to the hazard analysis and decision component 314
based on the fault information. Also, to further comply with safety
integrity level requirements, processor(s) 318 can be specified as
a SIL- or ASIL-rated processor to ensure that the imaging sensor
conforms to required safety standards.
[0065] Embodiments of the imaging sensor device 302 that include
sufficient safety components to meet or exceed a defined SIL or
ASIL rating can be suitably implemented as components of a safety
system (e.g., an industrial safety system, an on-board automotive
safety system, etc.). For example, embodiments of the imaging
sensor device described herein can be used in place of a light
curtain to perform safety detection in industrial environments. Use
of an imaging sensor device to perform safety monitoring can offer
advantages over traditional light curtains or other presence
sensing devices by supporting object identification and
classification, human recognition, and other features not supported
by conventional presence sensors. FIG. 9 is a schematic of an
industrial safety monitoring system that utilizes imaging sensor
device 302. In this example, a conveyor 912 transports products 914
into a protected machining or material handling area. Product 914
may comprise, for example, a manufactured part being conveyed
through a series of automated machining processes, a pallet
containing packaged products, or other such item. The machining or
material handling area includes a robot 902 operating under the
control and supervision of industrial controller 904, and thus
constitutes a hazardous area that must be rendered safe before a
human operator enters. The conveyor 912 transports the product 914
into the hazardous area via gateway 906 (e.g., an opening in a
safety fence that surrounds the hazardous area).
[0066] Imaging sensor device 302 is mounted such that the light
emitter and receiving lens face directly downward toward the
conveyor. The light beam 908 is aimed toward the area directly in
front of gateway 906. Using the technique described above, imaging
sensor device 302 can identify and classify objects passing through
light beam 908 as the objects approach gateway 906. Using this
configuration, imaging sensor device 302 can simulate light curtain
muting features, such that product 914 is permitted to pass through
gateway 906 without halting operation of robot 902, but detection
of a human operator approaching gateway 906 will cause the robot
902 to switch to a safe mode (e.g., halted or slowed).
[0067] To this end, the imaging sensor device 302 can identify and
classify objects passing through light beam 908 using one or more
techniques described above. For example, imaging sensor device 302
can be trained to identify product 914 as a first object class
("product"), and to identify human operator 910 as a second object
class ("human"). When product 914 passes through light beam 908,
imaging sensor device 302 can perform 2D analysis on the resulting
image to identify the object, determine that the object belongs to
the product classification, and allow the product 914 to proceed
through gateway 906 to the hazardous area without disabling the
robot 902 or conveyor 912. If an operator 910 standing on the
conveyor 912 passes through light beam 908, the imaging sensor
device 302 identifies the operator as belonging to the human
classification and generates a control output placing the system in
a safe mode. This may comprise, for example, sending a control
output to industrial controller 904 instructing the controller to
disable the robot 902 and to halt the conveyor 912.
[0068] To improve reliability of human detection while minimizing
processing load and maintaining acceptable response times, imaging
sensor device 302 can be configured to perform selective 3D
analysis on a narrow strip through the middle of the image frame
corresponding to the conveyor path, while performing faster 2D
analysis on the remaining portions of the image frame. Performing
depth (distance) analysis on the areas corresponding to the
conveyor path can assist the imaging sensor device 302 in
accurately identifying the presence of a human on the conveyor,
since object height can be used to distinguish between a valid
product 914 and a human being or an improper object on the conveyor
912.
[0069] In an alternative configuration, the imaging sensor device
302 may be configured to perform 2D analysis over the entire pixel
array of the image during normal operation until an object that
does not conform to the "product" classification enters the viewing
field. In response to detection of a non-product object entering
the viewing field, the pixel array component 306 of the sensor can
designate an area of the pixel array corresponding to the object
for 3D analysis in order to obtain height information and profile
over time for the new object, which can be used by the sensor to
assist in determining whether the new object corresponds to the
"human being" classification. The pixel array component 306 can
dynamically move the portion of the image designated for 3D
analysis to track with the object as it moves through the viewing
field (e.g., based on object detection information provided by the
image analysis component 312).
[0070] In another example of dynamic hazard analysis, the imaging
sensor device 302 may be configured to adjust the size of the 3D
analysis pixel area (e.g., the hazard zone) based on a current
hazard level determined via 2D image analysis. For example, an area
of the pixel array designated for 3D analysis may correspond to an
area surrounding a hazardous area (e.g., a machine or traffic
area). Based on 2D analysis of the pixel array, the imaging sensor
device 302 may determine a speed of objects (e.g., machine
components, mobile vehicles, objects on a conveyor, etc.) within
the hazardous area. If the speed of objects is determined to exceed
a threshold, implying a higher risk of injury, the portion of the
pixel array for which 3D analysis is performed may be increased to
a larger area surrounding the hazardous zone. When slower objects
are detected within the hazardous zone (e.g., when the determined
speed falls below the threshold), the risk level is assumed to be
lessened, and the 3D analysis portion of the pixel array is made
smaller to allow operators freedom to approach the hazardous area
more closely.
[0071] The configuration depicted in FIG. 9 has a number of
advantages over a light curtain solution. For example, light
curtain transmitters and receivers are typically mounted vertically
on either side of an entryway, exposing those components to
possible damage by passing objects. Mounting the imaging sensor
device 302 on the ceiling mitigates the risk of damage by placing
the monitoring device outside the reach of passing objects.
Moreover, light curtains are often muted to allow a product to pass
through the entryway at particular defined durations during the
operating cycle (that is, durations during which a product is
expected to pass through the light curtain), and enabled during the
remaining portions of the cycle. Since muting of the light curtain
in such scenarios is a function of the particular portion of the
operating cycle being executed, this method opens the possibility
that a human may pass through the light curtain undetected during
those times when the light curtain is muted. By contrast, since
imaging sensor device 302 is able to classify detected objects as
corresponding to a "human" classification and alter control based
on this object classification, the imaging sensor device 302 is
able to perform more direct and intelligent muting based on object
detection and classification rather than being cycle-dependent.
[0072] The imaging sensor device 302 can also be used in other
types of monitoring applications. For example, imaging sensor
device 302 can be used to monitor access to particular areas of an
industrial facility. In an example access monitoring application,
an imaging sensor device 302 can be mounted on the ceiling above an
entryway to an area to be monitored. Similar to the configuration
illustrated in FIG. 9, the imaging sensor device 302 can be
oriented to face downward to capture an image of the floor area
outside the entryway to the monitored area. Since imaging sensor
device 302 is able to accurately distinguish between humans and
non-human objects, plant personnel can be tracked and counted as
they enter and leave the monitored area. In an alternative
configuration, the imaging sensor device 302 can be mounted above
the entryway so that the sensor can additionally perform facial
recognition and employee identification as part of the image
analysis.
[0073] FIG. 10 illustrates an example automotive safety application
of the imaging sensor device described herein. In this example, an
imaging sensor device 1004 is mounted on a vehicle 1002 (e.g., on
the rearview mirror, the bumper, the grill, etc.) and oriented to
achieve a substantially horizontal viewing axis directed toward the
front of the vehicle. In this example, the imaging sensor device
1004 is communicatively connected to the vehicle's engine control
unit (ECU) over the car network and provides information or control
outputs to the ECU based on hazard analysis performed by the hazard
analysis and decision component 314 (based on image analysis
performed by the distance determination component 310 and image
analysis component). The safety component 316 also feeds data to
the ECU based on internal sensor device fault monitoring and
diagnostics. In particular, imaging sensor device 1004 performs
pedestrian detection by obtaining image for a viewing area in front
of the vehicle 1002 and applying the object detection and
classification techniques described above to identify pedestrians
1006 near the vehicle 1002. The imaging sensor device 1004 is also
trained to identify and classify other vehicles 1008 in the
vicinity. Using the image analysis techniques described herein, the
imaging sensor device can determine such factors as the position of
the pedestrians 1006 and vehicles 1008, speed and acceleration of
these detected objects relative to vehicle 1002, anticipated
trajectories of the objects, etc. Based on this data, the imaging
sensor device 1004 can output information and instructions to the
ECU in response to identified risks (e.g., a potential collision
with a pedestrian 1006 or vehicle 1008). For example, the imaging
sensor device 1004 may instruct the ECU to activate the vehicle's
braking system in response to a detected risk of collision with a
pedestrian 1006 or vehicle 1008. In this regard, the imaging sensor
device 1004 runs algorithms that detect, classify, and track
objects; analyze a level of risk; and decide whether the braking
system should be activated. The internal safety monitoring and
diagnostic functions carried out by the safety component 316 can
ensure that the imaging sensor device complies with appropriate
safety standards for automobile on-board safety systems (e.g., ISO
26262).
[0074] FIGS. 11-12 illustrate various methodologies in accordance
with one or more embodiments of the subject application. While, for
purposes of simplicity of explanation, the one or more
methodologies shown herein are shown and described as a series of
acts, it is to be understood and appreciated that the subject
innovation is not limited by the order of acts, as some acts may,
in accordance therewith, occur in a different order and/or
concurrently with other acts from that shown and described herein.
For example, those skilled in the art will understand and
appreciate that a methodology could alternatively be represented as
a series of interrelated states or events, such as in a state
diagram. Moreover, not all illustrated acts may be required to
implement a methodology in accordance with the innovation.
Furthermore, interaction diagram(s) may represent methodologies, or
methods, in accordance with the subject disclosure when disparate
entities enact disparate portions of the methodologies. Further
yet, two or more of the disclosed example methods can be
implemented in combination with each other, to accomplish one or
more features or advantages described herein.
[0075] FIG. 11 illustrates an example methodology 1100 for
performing selective three-dimensional analysis on a pixel array by
an imaging sensor device. Initially, at 1102, image data is
received at an imaging sensor device corresponding to an image of a
viewing area monitored by the device. The image data can be
obtained by emitting light illumination into the viewing area and
measuring the reflected light received by each pixel of the imaging
sensor device's photo-receiver array. At 1104, pixel array
information is generated by the imaging sensor device based on the
image data received at step 1102. The pixel array information can
collectively comprise pixel data for an image frame collected by
the imaging sensor device. At 1106, two-dimensional (2D) analysis
is performed on a first subset of the pixel array to at least one
of identify an object within the image, classify an object within
the image, or correlate two or more objects identified in the
image.
[0076] At 1108, three-dimensional (3D) analysis is performed on a
second subset of the pixel array to determine distance information
for spaces within the viewing area corresponding to the second
subset of the pixel array. In some embodiments, the second subset
of the pixel array on which 3D analysis is to be performed can be
defined by a system designer prior to operation and recorded in a
configuration profile, which can be read by a pixel array component
of the imaging sensor device in order to group the first and second
subsets of the pixel array for respective 2D and 3D analysis.
Alternatively, the imaging sensor device can dynamically select the
second subset of the pixel array for 3D analysis based on results
of the 2D analysis performed at step 1106. For example, if the 2D
analysis determines that an object of a certain classification has
entered the viewing field, the imaging sensor device may define an
area of the pixel array corresponding to the newly identified
object and begin performing 3D analysis on the object in order to
obtain spatial information for the object.
[0077] At 1110, at least one of a control output or feedback
information is generated by the imaging sensor device based on
correlation of information generated by the 2D analysis and the 3D
analysis. For example, the imaging sensor device may correlate the
2D and 3D analysis results to yield an identity position, speed,
acceleration, orientation, and/or trajectory for the object and
generate a control or message output based on one or more of these
measured factors. The control output may comprise, for example, an
instruction to an industrial controller to transition a hazardous
industrial machine to a safe mode, to lock an entry door to a
hazardous area to prevent access, or other such control output.
[0078] FIG. 12 illustrates an example methodology 1200 for
dynamically selecting a portion of a pixel array for selective 3D
analysis. Initially, at 1202, image data is received at an imaging
sensor device corresponding to an image of a viewing area monitored
by the device. At 1204, pixel array information is generated by the
imaging sensor device based on the image data received at step
1202. At 1206, 2D imaging analysis is performed on the pixel array.
At 1208, an object within the image is identified and classified
based on the 2D imaging analysis.
[0079] At 1210, a determination is made regarding whether the
classification of the object determined at step 1208 requires 3D
(distance) analysis. For example, the sensor device may be trained
to identify when a human has entered the viewing area. Accordingly,
the sensor can determine that an object having a "human"
classification has entered the viewing area based on the 2D
analysis and object classification performed at steps 1206 and
1208.
[0080] If the object classification does not require 3D analysis,
the methodology returns to step 1202 and continues monitoring
received image data. Alternatively, if the object classification is
determined to require 3D analysis, the methodology moves to step
1212, where a subset of the pixel array corresponding to an area of
the image surrounding the object is identified. At 1214, 3D
analysis is performed on the subset of the pixel array identified
at step 1212 in order to determine distance information for the
object. At 1216, at least one of a control output or feedback
information is generated by the imaging sensor device based on
correlation of information generated by the 2D analysis of step
1206 and the 3D analysis of step 1214. This may include, for
example, identifying a potentially hazardous condition or risk
based on correlation of the 2D and 3D results and sending an
instruction to a separate controller (e.g., an industrial
controller, a safety relay, a control computer of an automotive
vehicle, etc.) that is communicatively connected to the imaging
sensor to perform an action designed to mitigate the detected
hazard. The action may comprise, for example, switching an
industrial machine or system to a safe state (e.g., stopping the
machine, switching the machine to a slow operation mode, returning
the machine to the home position, etc.), instructing a vehicle's
braking system to slow or stop the vehicle, or other such
action.
[0081] Embodiments, systems, and components described herein, as
well as control systems and automation environments in which
various aspects set forth in the subject specification can be
carried out, can include computer or network components such as
servers, clients, programmable logic controllers (PLCs), automation
controllers, communications modules, mobile computers, on-board
computers of or mobile vehicles, wireless components, control
components and so forth which are capable of interacting across a
network. Computers and servers include one or more
processors--electronic integrated circuits that perform logic
operations employing electric signals--configured to execute
instructions stored in media such as random access memory (RAM),
read only memory (ROM), a hard drives, as well as removable memory
devices, which can include memory sticks, memory cards, flash
drives, external hard drives, and so on.
[0082] Similarly, the term PLC or automation controller as used
herein can include functionality that can be shared across multiple
components, systems, and/or networks. As an example, one or more
PLCs or automation controllers can communicate and cooperate with
various network devices across the network. This can include
substantially any type of control, communications module, computer,
Input/Output (I/O) device, sensor, actuator, and human machine
interface (HMI) that communicate via the network, which includes
control, automation, and/or public networks. The PLC or automation
controller can also communicate to and control various other
devices such as standard or safety-rated I/O modules including
analog, digital, programmed/intelligent I/O modules, other
programmable controllers, communications modules, sensors,
actuators, output devices, and the like.
[0083] The network can include public networks such as the
internet, intranets, and automation networks such as control and
information protocol (CIP) networks including DeviceNet,
ControlNet, safety networks, and Ethernet/IP. Other networks
include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus, Profibus,
CAN, wireless networks, serial protocols, and so forth. In
addition, the network devices can include various possibilities
(hardware and/or software components). These include components
such as switches with virtual local area network (VLAN) capability,
LANs, WANs, proxies, gateways, routers, firewalls, virtual private
network (VPN) devices, servers, clients, computers, configuration
tools, monitoring tools, and/or other devices.
[0084] In order to provide a context for the various aspects of the
disclosed subject matter, FIGS. 13 and 14 as well as the following
discussion are intended to provide a brief, general description of
a suitable environment in which the various aspects of the
disclosed subject matter may be implemented.
[0085] With reference to FIG. 13, an example environment 1310 for
implementing various aspects of the aforementioned subject matter
includes a computer 1312. The computer 1312 includes a processing
unit 1314, a system memory 1316, and a system bus 1318. The system
bus 1318 couples system components including, but not limited to,
the system memory 1316 to the processing unit 1314. The processing
unit 1314 can be any of various available processors. Multi-core
microprocessors and other multiprocessor architectures also can be
employed as the processing unit 1314.
[0086] The system bus 1318 can be any of several types of bus
structure(s) including the memory bus or memory controller, a
peripheral bus or external bus, and/or a local bus using any
variety of available bus architectures including, but not limited
to, 8-bit bus, Industrial Standard Architecture (ISA),
Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent
Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component
Interconnect (PCI), Universal Serial Bus (USB), Advanced Graphics
Port (AGP), Personal Computer Memory Card International Association
bus (PCMCIA), and Small Computer Systems Interface (SCSI).
[0087] The system memory 1316 includes volatile memory 1320 and
nonvolatile memory 1322. The basic input/output system (BIOS),
containing the basic routines to transfer information between
elements within the computer 1312, such as during start-up, is
stored in nonvolatile memory 1322. By way of illustration, and not
limitation, nonvolatile memory 1322 can include read only memory
(ROM), programmable ROM (PROM), electrically programmable ROM
(EPROM), electrically erasable PROM (EEPROM), or flash memory.
Volatile memory 1320 includes random access memory (RAM), which
acts as external cache memory. By way of illustration and not
limitation, RAM is available in many forms such as synchronous RAM
(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data
rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM
(SLDRAM), and direct Rambus RAM (DRRAM).
[0088] Computer 1312 also includes removable/non-removable,
volatile/nonvolatile computer storage media. FIG. 13 illustrates,
for example a disk storage 1324. Disk storage 1324 includes, but is
not limited to, devices like a magnetic disk drive, floppy disk
drive, tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory
card, or memory stick. In addition, disk storage 1324 can include
storage media separately or in combination with other storage media
including, but not limited to, an optical disk drive such as a
compact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive),
CD rewritable drive (CD-RW Drive) or a digital versatile disk ROM
drive (DVD-ROM). To facilitate connection of the disk storage 1324
to the system bus 1318, a removable or non-removable interface is
typically used such as interface 1126.
[0089] It is to be appreciated that FIG. 13 describes software that
acts as an intermediary between users and the basic computer
resources described in suitable operating environment 1310. Such
software includes an operating system 1328. Operating system 1328,
which can be stored on disk storage 1324, acts to control and
allocate resources of the computer 1312. System applications 1330
take advantage of the management of resources by operating system
1328 through program modules 1332 and program data 1334 stored
either in system memory 1316 or on disk storage 1324. It is to be
appreciated that one or more embodiments of the subject disclosure
can be implemented with various operating systems or combinations
of operating systems.
[0090] A user enters commands or information into the computer 1312
through input device(s) 1336. Input devices 1336 include, but are
not limited to, a pointing device such as a mouse, trackball,
stylus, touch pad, keyboard, microphone, joystick, game pad,
satellite dish, scanner, TV tuner card, digital camera, digital
video camera, web camera, and the like. These and other input
devices connect to the processing unit 1314 through the system bus
1318 via interface port(s) 1338. Interface port(s) 1338 include,
for example, a serial port, a parallel port, a game port, and a
universal serial bus (USB). Output device(s) 1340 use some of the
same type of ports as input device(s) 1336. Thus, for example, a
USB port may be used to provide input to computer 1312, and to
output information from computer 1312 to an output device 1340.
Output adapters 1342 are provided to illustrate that there are some
output devices 1340 like monitors, speakers, and printers, among
other output devices 1340, which require special adapters. The
output adapters 1342 include, by way of illustration and not
limitation, video and sound cards that provide a means of
connection between the output device 1340 and the system bus 1318.
It should be noted that other devices and/or systems of devices
provide both input and output capabilities such as remote
computer(s) 1344.
[0091] Computer 1312 can operate in a networked environment using
logical connections to one or more remote computers, such as remote
computer(s) 1344. The remote computer(s) 1344 can be a personal
computer, a server, a router, a network PC, a workstation, a
microprocessor based appliance, a peer device or other common
network node and the like, and typically includes many or all of
the elements described relative to computer 1312. For purposes of
brevity, only a memory storage device 1346 is illustrated with
remote computer(s) 1344. Remote computer(s) 1344 is logically
connected to computer 1312 through a network interface 1348 and
then physically connected via communication connection 1350.
Network interface 1348 encompasses communication networks such as
local-area networks (LAN) and wide-area networks (WAN). LAN
technologies include Fiber Distributed Data Interface (FDDI),
Copper Distributed Data Interface (CDDI), Ethernet/IEEE 802.3,
Token Ring/IEEE 802.5 and the like. WAN technologies include, but
are not limited to, point-to-point links, circuit switching
networks like Integrated Services Digital Networks (ISDN) and
variations thereon, packet switching networks, and Digital
Subscriber Lines (DSL).
[0092] Communication connection(s) 1350 refers to the
hardware/software employed to connect the network interface 1348 to
the system bus 1318. While communication connection 1350 is shown
for illustrative clarity inside computer 1312, it can also be
external to computer 1312. The hardware/software necessary for
connection to the network interface 1348 includes, for exemplary
purposes only, internal and external technologies such as, modems
including regular telephone grade modems, cable modems and DSL
modems, ISDN adapters, and Ethernet cards.
[0093] FIG. 14 is a schematic block diagram of a sample computing
environment 1400 with which the disclosed subject matter can
interact. The sample computing environment 1400 includes one or
more client(s) 1402. The client(s) 1402 can be hardware and/or
software (e.g., threads, processes, computing devices). The sample
computing environment 1400 also includes one or more server(s)
1404. The server(s) 1404 can also be hardware and/or software
(e.g., threads, processes, computing devices). The servers 1404 can
house threads to perform transformations by employing one or more
embodiments as described herein, for example. One possible
communication between a client 1402 and servers 1404 can be in the
form of a data packet adapted to be transmitted between two or more
computer processes. The sample computing environment 1400 includes
a communication framework 1406 that can be employed to facilitate
communications between the client(s) 1402 and the server(s) 1404.
The client(s) 1402 are operably connected to one or more client
data store(s) 1408 that can be employed to store information local
to the client(s) 1402. Similarly, the server(s) 1404 are operably
connected to one or more server data store(s) 1410 that can be
employed to store information local to the servers 1404.
[0094] What has been described above includes examples of the
subject innovation. It is, of course, not possible to describe
every conceivable combination of components or methodologies for
purposes of describing the disclosed subject matter, but one of
ordinary skill in the art may recognize that many further
combinations and permutations of the subject innovation are
possible. Accordingly, the disclosed subject matter is intended to
embrace all such alterations, modifications, and variations that
fall within the spirit and scope of the appended claims.
[0095] In particular and in regard to the various functions
performed by the above described components, devices, circuits,
systems and the like, the terms (including a reference to a
"means") used to describe such components are intended to
correspond, unless otherwise indicated, to any component which
performs the specified function of the described component (e.g., a
functional equivalent), even though not structurally equivalent to
the disclosed structure, which performs the function in the herein
illustrated exemplary aspects of the disclosed subject matter. In
this regard, it will also be recognized that the disclosed subject
matter includes a system as well as a computer-readable medium
having computer-executable instructions for performing the acts
and/or events of the various methods of the disclosed subject
matter.
[0096] In addition, while a particular feature of the disclosed
subject matter may have been disclosed with respect to only one of
several implementations, such feature may be combined with one or
more other features of the other implementations as may be desired
and advantageous for any given or particular application.
Furthermore, to the extent that the terms "includes," and
"including" and variants thereof are used in either the detailed
description or the claims, these terms are intended to be inclusive
in a manner similar to the term "comprising."
[0097] In this application, the word "exemplary" is used to mean
serving as an example, instance, or illustration. Any aspect or
design described herein as "exemplary" is not necessarily to be
construed as preferred or advantageous over other aspects or
designs. Rather, use of the word exemplary is intended to present
concepts in a concrete fashion.
[0098] Various aspects or features described herein may be
implemented as a method, apparatus, or article of manufacture using
standard programming and/or engineering techniques. The term
"article of manufacture" as used herein is intended to encompass a
computer program accessible from any computer-readable device,
carrier, or media. For example, computer readable media can include
but are not limited to magnetic storage devices (e.g., hard disk,
floppy disk, magnetic strips . . . ), optical disks [e.g., compact
disk (CD), digital versatile disk (DVD) . . . ], smart cards, and
flash memory devices (e.g., card, stick, key drive . . . ).
* * * * *